Cycle Self-Training With Joint Adversarial for Cross-Scene Hyperspectral Image Classification

Cross-scene hyperspectral image classification (HSIC) leverages existing knowledge to categorize unknown scenes, aligning with the practical applications of remote sensing monitoring. However, spectral shifts across domains pose substantial challenges for this classification, aggravated by the insufficient number of labeled samples. Most existing methods predominantly address domain alignment from a singular perspective, rendering them inadequate to sustain robust classification performance in the presence of significant domain shifts. Additionally, although self-training can mitigate the labeling deficiency by leveraging unlabeled data, existing methods often fail to ensure the effective and accurate utilization of such data. Consequently, this article proposes a hyperspectral image (HSI) cross-scene classification architecture based on cycle self-training with joint adversarial (CSJA), which mitigates the impact of spectral shifts on cross-scene classification. Specifically, the proposed approach incorporates domain adversarial modules to reconcile domain distributions at varying granularities, coupled with a class adversarial module for joint adversarial alignment. Moreover, the cycle self-training (CST) module is devised to explicitly enforce pseudo-label generalization, thereby fully harnessing the informative content of the target domain. To effectively exploit both spatial and spectral information in HSIs and extract discriminative features, a convolutionally enhanced Transformer feature extraction network is proposed to generate feature-rich representations for both domains. Experimental evaluations on two publicly available cross-scene hyperspectral datasets and two self-made UAV hyperspectral datasets validate the superiority of the proposed algorithm.